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In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has demonstrated high efficacy in several NLP tasks, especially in few-shot settings.
Lcsts: A large scale chinese short text summarization dataset
Baotian Hu, Qingcai Chen, and Fangze Zhu. 2015 · 1972
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, et al. 2013 · 2013
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. 2017 · 2017
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, et al. 2019 · 2019
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Predicting the type and target of offensive posts in social media
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, et al. 2019 · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, et al. 2019 · 2019
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. 2020 · 2020
Earlier work this paper cites.
Badnl: Backdoor attacks against nlp models
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang. 2021 · 2021
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Backdoor attacks on pre-trained models by layerwise weight poisoning
Linyang Li, Demin Song, Xiaonan Li, Jiehang Zeng, and Ruotian Ma. 2021 · 2021
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Onion: A simple and effective defense against textual backdoor attacks
Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, et al. 2021a · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
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Gpt-neox-20b: An open-source autoregressive language model
Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, et al. 2022 · 2022
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Badprompt: Backdoor attacks on continuous prompts
Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, et al. 2022 · 2022
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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, et al. 2022 · 2022
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Improving in-context few-shot learning via self-supervised training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, and Zornitsa Kozareva. 2022a · 2022
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Kallima: A clean-label framework for textual backdoor attacks
Xiaoyi Chen, Yinpeng Dong, Zeyu Sun, Shengfang Zhai, Qingni Shen, and Zhonghai Wu. 2022b · 2022
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, et al. 2022 · 2022
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Ppt: Backdoor attacks on pre-trained models via poisoned prompt tuning
Wei Du, Yichun Zhao, Boqun Li, Gongshen Liu, and Shilin Wang. 2022 · 2022
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Triggerless backdoor attack for nlp tasks with clean labels
Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, et al. 2022 · 2022
Earlier work this paper cites.
Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Mądry, and Bo Li. 2022 · 2022
Cited alongside, same era.
Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. 2022 · 2022
Cited alongside, same era.
Badhash: Invisible backdoor attacks against deep hashing with clean label
Shengshan Hu, Ziqi Zhou, Yechao Zhang, Leo Yu Zhang, Yifeng Zheng, et al. 2022 · 2022
Cited alongside, same era.
Trojtext: Test-time invisible textual trojan insertion
Qian Lou, Yepeng Liu, and Bo Feng. 2022 · 2022
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Cited alongside, same era.
Transformers as algorithms: Generalization and stability in in-context learning
Yingcong Li, Muhammed Emrullah Ildiz, Dimitris Papailiopoulos, and Samet Oymak. 2023 · 2023
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Autodan: Generating stealthy jailbreak prompts on aligned large language models
Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. 2023 · 2023
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Test-time backdoor mitigation for black-box large language models with defensive demonstrations
Wenjie Mo, Jiashu Xu, Qin Liu, Jiongxiao Wang, Jun Yan, Chaowei Xiao, and Muhao Chen. 2023 · 2023
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In-context example selection with influences
Tai Nguyen and Eric Wong. 2023 · 2023
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Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Cited alongside, same era.
Improving neural cross-lingual abstractive summarization via employing optimal transport distance for knowledge distillation
Thong Thanh Nguyen and Anh Tuan Luu. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
Exploring the universal vulnerability of prompt-based learning paradigm
Lei Xu, Yangyi Chen, Ganqu Cui, Hongcheng Gao, and Zhiyuan Liu. 2022 · 2022
Cited alongside, same era.
Planning with large language models for code generation
Shun Zhang, Zhenfang Chen, Yikang Shen, et al. 2022a · 2022
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2022
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, et al. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Later among the works it cites.
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, et al. 2023 · 2023
Later among the works it cites.
Hijacking large language models via adversarial in-context learning
Yao Qiang, Xiangyu Zhou, and Dongxiao Zhu. 2023 · 2023
Later among the works it cites.
Measuring inductive biases of in-context learning with underspecified demonstrations
Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, and He He. 2023 · 2023
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Introducing mpt-7b: A new standard for open-source, commercially usable llms
MosaicML NLP Team. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, et al. 2023 · 2023
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Poisoning language models during instruction tuning
Alexander Wan, Eric Wallace, Sheng Shen, and Dan Klein. 2023 · 2023
Later among the works it cites.
Haoran Wang and Kai Shu. 2023 · 2023
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Badchain: Backdoor chain-of-thought prompting for large language models
Zhen Xiang, Fengqing Jiang, Zidi Xiong, Bhaskar Ramasubramanian, et al. 2023 · 2023
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Poisonprompt: Backdoor attack on prompt-based large language models
Hongwei Yao, Jian Lou, and Zhan Qin. 2023 · 2023
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Compositional exemplars for in-context learning
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, et al. 2023 · 2023
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Prompt as triggers for backdoor attack: Examining the vulnerability in language models
Shuai Zhao, Jinming Wen, Luu Anh Tuan, Junbo Zhao, and Jie Fu. 2023b · 2023
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Backdoor attacks on dense passage retrievers for disseminating misinformation
Quanyu Long, Yue Deng, LeiLei Gan, Wenya Wang, and Sinno Jialin Pan. 2024 · 2024
Closest in time.
Atlantis: Aesthetic-oriented multiple granularities fusion network for joint multimodal aspect-based sentiment analysis
Luwei Xiao, Xingjiao Wu, Junjie Xu, Weijie Li, Cheng Jin, and Liang He. 2024 · 2024
Closest in time.
Defending against weight-poisoning backdoor attacks for parameter-efficient fine-tuning
Shuai Zhao, Leilei Gan, Luu Anh Tuan, Jie Fu, Lingjuan Lyu, Meihuizi Jia, and Jinming Wen. 2024b · 2024
Closest in time.